Concept

Gaz neuronal

Publications associées (22)

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

New York2023

Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots

Alcherio Martinoli, Chiara Ercolani, Faezeh Rahbar

The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly refe ...

MDPI2023

4M: Massively Multimodal Masked Modeling

Shuqing Teresa Yeo, Amir Roshan Zamir, Oguzhan Fatih Kar, Roman Christian Bachmann, David Mizrahi

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

Neural Information Processing Systems (Nips)2023

A novel recursive algorithm used to model hardware programmable neighborhood mechanism of self-organizing neural networks

Rafal Tomasz Dlugosz

In this paper we propose a novel recursive algorithm that models the neighborhood mechanism, which is commonly used in self-organizing neural networks (NNs). The neighborhood can be viewed as a map of connections between particular neurons in the NN. Its r ...

Elsevier2015

Organizing neural networks

Henry Markram, Rodrigo de Campos Perin, Thomas Berger

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for organizing trained and untrained neural networks. In one aspect, a neural network device includes a collection of node assemblies interconnected by betwe ...

2011

An influence of current-leakage in analog memory on training Kohonen neural network implemented on silicon

Rafal Tomasz Dlugosz

The paper presents how the current leakage encountered in capacitive analog memories affects the learning process of hardware implemented Kohonen neural networks (KNN). MOS transistor leakage currents, which strongly depend on temperature, increase the net ...

2010

Realization of the Conscience Mechanism in CMOS Implementation of Winner-Takes-All Self-Organizing Neural Networks

Rafal Tomasz Dlugosz

This paper presents a complementary metal–oxide– semiconductor (CMOS) implementation of a conscience mechanism used to improve the effectiveness of learning in the winnertakes- all (WTA) artificial neural networks (ANNs) realized at the transistor level. T ...

Institute of Electrical and Electronics Engineers2010

A New Fast Training Algorithm of the WTM Kohonen Neural Network Implemented for Classification of Biomedical Signals

Rafal Tomasz Dlugosz

A new optimized algorithm for the learning process suitable for hardware implemented Winner Takes Most Kohonen Neural Network (KNN) has been proposed in the paper. In networks of this type a neighborhood mechanism is used to improve the convergence propert ...

2009

New Fast Training Algorithm Suitable For Hardware Kohonen Neural Networks Designed For Analysis Of Biomedical Signals

Rafal Tomasz Dlugosz

Insticc-Inst Syst Technologies Information Control & Communication, Avenida D Manuel L, 27A 2 Esquerdo, Setubal, 2910-595, Portugal2009

SOM-based Clustering of Multilingual Documents Using an Ontology

Minh Hai Pham

Clustering similar documents is a difficult task for text data mining. Difficulties stem especially from the way documents are translated into numerical vectors. In this chapter, we will present a method that uses Self Organizing Map (SOM) to cluster medic ...

Information Science Reference2008

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search